apachecn/ailearning

★ 42,262PythonAudience · developerComplexity · 2/5StaleLicenseSetup · easy

Mindmap

mindmap
  root((repo))
    What it covers
      Classical ML algorithms
      Deep learning basics
      Natural language processing
    Learning format
      Code walkthroughs
      Video tutorials
      Readable notes
    Tech stack
      Python
      scikit-learn
      PyTorch and TensorFlow
    Use cases
      Learn ML fundamentals
      Study deep learning
      Practice NLP tasks
    Audience
      Chinese developers
      ML beginners
      Self-study learners

mindmap root((repo)) What it covers Classical ML algorithms Deep learning basics Natural language processing Learning format Code walkthroughs Video tutorials Readable notes Tech stack Python scikit-learn PyTorch and TensorFlow Use cases Learn ML fundamentals Study deep learning Practice NLP tasks Audience Chinese developers ML beginners Self-study learners

Things people build with this

USE CASE 1

Learn classical machine learning algorithms like KNN, decision trees, and support vector machines with working Python code.

USE CASE 2

Study deep learning fundamentals including CNNs, RNNs, and LSTMs with PyTorch and TensorFlow examples.

USE CASE 3

Practice natural language processing tasks like tokenization and named entity recognition using NLTK.

Tech stack

Pythonscikit-learnPyTorchTensorFlow 2.0NLTK

Getting it running

Difficulty · easy Time to first run · 5min

Open-source educational materials created and maintained by the ApacheCN volunteer community for free use and distribution.

In plain English

AiLearning is a Chinese-language educational resource repository from ApacheCN, a volunteer-driven open-source community focused on translating and creating AI learning materials for Chinese-speaking developers. With over 42,000 stars, it is one of the most popular AI self-study collections on GitHub for that audience. The repository addresses a common frustration: many people who want to learn machine learning and data analysis are not fluent enough in English to follow popular courses by instructors like Andrew Ng, or find the abstract mathematical derivations in academic materials difficult to connect to practical code. AiLearning bridges that gap by providing Chinese-language notes, code walkthroughs, and video tutorials based on accessible textbooks. The content is organized into three main sections. The first covers classical machine learning using the book "Machine Learning in Action", topics include KNN (K-nearest neighbors, a method that classifies things by finding similar examples), decision trees, Naive Bayes, logistic regression, support vector machines, k-means clustering, and association-rule algorithms like Apriori and FP-growth. The second section covers deep learning fundamentals, backpropagation, CNNs (convolutional neural networks, the type used for image recognition), RNNs and LSTMs (recurrent architectures for sequences), with tutorials using both PyTorch and TensorFlow 2.0. The third section introduces natural language processing (NLP), working with text, including tokenization, part-of-speech tagging, and named entity recognition using the NLTK library. You would turn to this repository if you are a Chinese-speaking developer new to machine learning who wants readable, code-focused explanations rather than dense theory. The materials pair well with video series hosted on Bilibili and other Chinese platforms. The tech stack is Python, with examples that use scikit-learn, PyTorch, TensorFlow 2.0, and NLTK.

Copy-paste prompts

Prompt 1

Show me how to implement K-nearest neighbors classification using the code examples from this AiLearning repository.

Prompt 2

Walk me through the PyTorch CNN tutorial in this repo and explain how convolutional layers work for image recognition.

Prompt 3

How do I use the NLTK examples in this repository to perform named entity recognition on Chinese text?

Prompt 4

Explain the backpropagation examples in this deep learning section and how they connect to the TensorFlow 2.0 code.

Prompt 5

Help me understand the decision tree and Naive Bayes implementations in the classical ML section of this repository.

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